域内不变特征(internally-invariant features),与分类有关的特征,产生于域的内部,不受其他域的影响,主要抓取数据的内在语义信息; 域间不变特征(mutually-invariant features),跨域迁移知识,通过多个域产生,共同学习的一些知识;本文认为,把这两种特征有效充分地结合起来,可以得到泛化性更好的模型。注意我们的方法类似...
Disclosed are a domain-invariant feature-based meta-knowledge fine-tuning method and a platform. In the method, highly-transferable common knowledge, that is, a domain-invariant feature, is learned on different data sets of similar tasks; and common domain features on different domains corresponding...
Unsupervised domain adaptation (UDA) mainly explores how to learn domain-invariant features from the source domain when the target domain label is unknown. To learn domain-invariant features requires aligning the distribution of samples from two domains in the feature space, which can be achieved by...
领域泛化的主要挑战是克服多个训练域和不可见测试域之间的潜在分布转移,一种流行的DG算法旨在学习跨训练域具有不变因果关系的表示,然而,某些特征,称为伪不变特征 pseudo-invariant features,可能在训练域是不变的,但在测试域不是,并会大大降低现有算法的性能。为了解决这个问题,我们提出了一种新的算法,称为 Invaria...
This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relation...
Hand-crafted features based on linguistic and domain-knowledge play crucial role in determining the performance of disease name recognition systems. Such methods are further limited by the scope of these features or in other words, their ability to cover the contexts or word dependencies within a ...
International Conference on Machine Learning|June 2019 Download BibTex Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hop...
ADVERSARIAL LEARNING OF RAW SPEECH FEATURES FOR DOMAIN INVARIANTSPEECH RECOGNITIONAditay Tripathi?‡Aanchan Mohan†Saket Anand?Maneesh Singh‡?Indraprastha Institute of Information Technology, New Delhi, India.†Synaptitude Brain Health, Vancouver, Canada.‡Verisk Analytics, Jersey City, USA....
step 3 : Train Cross-domain Autoencoder python train.py \ --datasets_dir SAM --model_exit model/ Here, --datasets_dir is the path of features and masks extracted from SAM. step 4 : Classification Run feature/classification.ipynb to get the classification results.About...
In order to make the model learn inter-domain-invariant features, we only deal with one gap at a time during the training process, adopting an alternating training strategy. For the style gap, we use the “style-invariant” loss to force the model to output the same prediction results ...